Underwater 3D shape measurement using inverse triangulation through two flat refractive surfaces

Buschinelli, Pedro; Matos, Gabriel; Pinto, Tiago; Albertazzi, Armando

doi:10.1109/oceans.2016.7761231

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…In real tasks, acquisition angles of less than 45° with respect to the targets on the calibration fixture often are avoided, since targets might not be measurable anymore, respectively have large elliptical eccentricity. Computer vision approaches often use such flat test-fields in the form of chessboard targets [37,38]. According to [29], 2D test-field calibrations result in less accurate and less robust parameter estimation.…”

Section: Dataset Hsmentioning

confidence: 99%

Impact of Stereo Camera Calibration to Object Accuracy in Multimedia Photogrammetry

2020

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Camera calibration via bundle adjustment is a well-established standard procedure in single-medium photogrammetry. When using standard software and applying the collinearity equations in multimedia photogrammetry, the effects of refractive interfaces are compensated in an implicit form, hence by the usual parameters of interior orientation. This contribution analyses different calibration strategies for planar bundle-invariant interfaces. To evaluate the effects of implicitly modelling the refractive effects within bundle adjustment, synthetic error-free datasets are simulated. The behaviour of interior, exterior, and relative orientation parameters is analysed using synthetic datasets free of underwater imaging effects. A shift of the camera positions of 0.2% of the acquisition distance along the optical axis can be observed. The relative orientation of a stereo camera shows systematic effects when the angle of convergence varies. The stereo baseline increases by 1% at 25° convergence. Furthermore, the interface is set up at different distances to the camera. When the interface is at 50% distance assuming a parallel camera setup, the stereo baseline also increases by 1%. It becomes clear that in most cases the implicit modelling is not suitable for multimedia photogrammetry due to geometrical errors (scaling) and absolute positioning errors. Explicit modelling of the refractive interfaces is implemented into a bundle adjustment and is also used to analyse calibration parameters and deviations in object space. Real experiments show that it is difficult to separate the effects of implicit modelling, since other effects, such as poor image measurements, affect the final result. However, trends can be seen, and deviations are quantified.

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Section: Dataset Hsmentioning

confidence: 99%

Impact of Stereo Camera Calibration to Object Accuracy in Multimedia Photogrammetry

2020

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“…Some systems are left out of this analysis because they follow a hybrid approach of combining active and passive light techniques. For instance, some stereo cameras make use of active light projection [113,131,132] in order to ease the feature finding and matching processes. Duda et al [133] use an iterative combination of active light projection with sfm.…”

Section: Quantitative Analysis Of Current Technologiesmentioning

confidence: 99%

State of the Art of Underwater Active Optical 3D Scanners

Castillón

Palomer

Forest

et al. 2019

Sensors

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Underwater inspection, maintenance and repair (IMR) operations are being increasingly robotized in order to reduce safety issues and costs. These robotic systems rely on vision sensors to perform fundamental tasks, such as navigation and object recognition and manipulation. Especially, active optical 3D scanners are commonly used due to the domain-specific challenges of underwater imaging. This paper presents an exhaustive survey on the state of the art of optical 3D underwater scanners. A literature review on light projection and light-sensing technologies is presented. Moreover, quantitative performance comparisons of underwater 3D scanners present in the literature and commercial products are carried out.

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“…Different colors represent the deviation in the 3D comparison. [15] Photogrammetry Flatness and length error 0.1-0.4 Zhang 2011 [24] Fringe projection Length error 0.5 Bruno et al 2011 [23] Fringe projection Flatness error 0.5 Bianco et al 2013 [25] Fringe projection Flatness and length error 0.1-0.3 Buschinelli et al 2016 [38] Fringe projection Length error (ball-bar) 0.4 1 percentage error was estimated by the authors from the published error values.…”

Section: Experimental Evaluationmentioning

confidence: 99%

A-Priori Calibration of a Structured Light Underwater 3D Sensor

Bräuer-Burchardt

Munkelt

Gebhart

et al. 2020

JMSE

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In this study, we introduce a new calibration method for underwater optical stereo scanners. It uses air calibration, additional underwater parameters, and extended camera modeling. The new methodology can be applied to both passive photogrammetric and structured light three-dimensional (3D) scanning systems. The novel camera model uses a variable principal distance depending on the radial distance to the principal point instead of two-dimensional distortion functions. This allows for an initial improvement of 3D reconstruction quality. In a second step, certain underwater-specific parameters—such as refraction indices, glass thickness, and view-port distances—are determined. Finally, a correction function for the entire measurement volume can be obtained from a few underwater measurements. Its application further improves the measurement accuracy. Measurement examples show the performance of the new calibration method in comparison to current underwater calibration strategies. A discussion of the possibilities and limits of the new calibration method and an outlook for future work complete this work.

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Underwater 3D shape measurement using inverse triangulation through two flat refractive surfaces

Cited by 8 publications

References 17 publications

Impact of Stereo Camera Calibration to Object Accuracy in Multimedia Photogrammetry

Impact of Stereo Camera Calibration to Object Accuracy in Multimedia Photogrammetry

State of the Art of Underwater Active Optical 3D Scanners

A-Priori Calibration of a Structured Light Underwater 3D Sensor

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